Apache Kafka

What Kafka does and why it matters

Apache Kafka is a distributed event streaming platform originally developed at LinkedIn to handle their real-time data feeds. It functions as a massively scalable, fault-tolerant commit log that decouples your systems from each other. Instead of point-to-point integrations where system A pushes data directly to system B, every system publishes events to Kafka topics and every interested system consumes them independently.

This fundamental shift changes how your architecture works. Systems no longer need to know about each other. New consumers can be added without modifying producers. Historical events can be replayed to rebuild state or backfill new systems. Real-time and batch processing can operate on the same data stream. The result is an architecture that is more resilient, more scalable and dramatically easier to evolve.

Kafka processes trillions of messages per day at companies including LinkedIn, Netflix, Uber, Spotify, Goldman Sachs and the New York Times. It is the de facto standard for event streaming in enterprise architecture.

How we deploy Kafka for business automation

We position Kafka as the central nervous system for event-driven automation. When a customer places an order, that event flows through Kafka to simultaneously trigger inventory updates, payment processing, fulfilment workflows in Airflow, real-time analytics in Flink and CRM updates - all without any of those systems being directly coupled to each other.

For AI-driven automation, Kafka provides the real-time data feeds that machine learning models consume for inference. Streaming sensor data, user behaviour events and transactional data arrive at your models within milliseconds, enabling decisions and actions in real time rather than batch processing overnight.

Key capabilities we implement

Durable event storage - Kafka persists events to disk with configurable retention, providing a complete, replayable history of everything that happened in your business. Events are replicated across multiple brokers for fault tolerance, ensuring no data loss even during hardware failures.

Real-time stream processing - with Kafka Streams and ksqlDB, transform, filter, aggregate and enrich data streams in real time without additional infrastructure. Build materialised views that maintain running totals, moving averages and windowed aggregations continuously.

Exactly-once semantics - guarantee that events are processed exactly once, even in the presence of failures and retries. Critical for financial transactions, inventory management and any workflow where duplicates or lost messages have business consequences.

Schema management - enforce data contracts between producers and consumers using the Schema Registry with Avro, Protobuf or JSON Schema. Ensure data quality at the platform level and enable safe schema evolution without breaking downstream consumers.

Kafka Connect - integrate with external systems using a library of pre-built connectors. Stream data from databases using change data capture (CDC), push events to data warehouses, synchronise with search engines and connect to hundreds of other systems without custom code.

Multi-tenancy and security - enforce access controls, encryption in transit and at rest, and topic-level permissions. Multiple teams and applications share the same Kafka cluster safely with resource quotas and namespace isolation.

Kafka in your automation stack

Kafka sits at the centre of the automation platform, feeding real-time events to Airflow for orchestration, to Flink for stream processing, to Spark for analytics and to your AI models for inference. APISIX manages the API layer for producers and consumers, while Superset visualises streaming metrics. Node architects, deploys and operates this entire event-driven infrastructure.